Writing Assignment #2 – Cultural Analytics

Zach Kleinbaum

Writing Assignment #2 – Evaluating a Cultural Analytics Project

When Lev Manovich first coined the term “Cultural Analytics” in 2005, he established a new means of studying the Digital Humanities. Cultural Analytics attempts to provide previously unquantifiable aspects of human culture, such as trends in artwork, television, video games, and social media, with quantifiable data so that it can be analyzed for patterns and statistics. The goal is to then display this cultural data in aesthetically pleasing and easily interpretable manners. The findings are presented visually, usually on high quality LCD screens or other imaging platforms. The goal of Cultural Analytics is to make previously unobtainable cultural data more open to trend analysis through the development of software and visual data representations.

One example of a project in Cultural Analytics is “Making Visible the Invisible” by George Legrady. Every project developed using Cultural Analytics requires a main research question, or problem at hand. Legrady’s work is designed  so that both customers and librarians alike can determine what works are popular at any given moment in time, to accomplish this task Legrady generates data on the books and other forms of media, like DVD and VHS, being checked out at the Seattle Central LIbrary. The information can be useful for several reasons, such as evaluation of trends in societal preferences or to the relationship between current events and the reading of patrons at the library. The data is then displayed using four different styles of visuals across six LCD screens located above the main librarian information desk in the library’s “Mixing Chamber”, an area dedicated to information retrieval and public research.

Accessibility and interpretability are characteristics used to judge the effectiveness of Cultural Analytics works, and Legrady succeeds in both of those categories. The location of the screens are easily accessible and centralized, considering that they are situated in the libraries’ main data center making the information relevant for patrons conducting research there. With regards to interpretability, Legrady is able to accumulate data on the materials being checked out of the library based on the “Dewey Decimal System”, which is a popular method of categorization used by libraries, as well as keywords found in the text of the respective works, and check out time. He then uses four styles of visualization to present the data. The first graphic is a count of vital statistics, such as the total number of items checked out, names of books checked out, and totals of fiction vs. nonfiction items. The second is a floating list of titles being checked out, which are organized chronologically based on check out time and color coded by genre. The third is a dot matrix that organizes the titles by the Dewey classification system. The final visual is a keyword map that connects the recently checked out titles by the presence of  keywords, and shows the distribution of keywords based on Dewey categories of the titles, for example books may be connected based on the presence of words like “1941” or “winter” . Overall, the collection of all four of these visuals provides the viewer with an assortment of useful data that benefits their visit to the library.

I thought that overall, John Legrady’s project is an exemplary representation of Cultural Analytics and how it can be beneficial to society. Legrady’s work at the library aids in the ability of patrons to make informed decisions based on the preferences and trends regarding the choices of their peers. The success of the project can be quantified by its lifespan as it lasted nearly nine years in the library from 2005 until 2014. Longevity is another characteristic of successful projects along with accessibility and interpretability.

Works Cited:

Forbes, Angus. “Cultural Analytics.” http://angusforbes.com/blog/cultural-analytics/

Legrady, George. “Making Visible the Invisible, 2005 – 2014.” http://georgelegrady.com

Manovich, Lev. “How and why study big cultural data.” http://lab.softwarestudies.com/2008/09/cultural-analytics.html